GraphIR: A Canonical Graph Intermediate Representation for Power-Grid Control

Graph neural networks are increasingly used for power-grid control, yet Grid2Op observations are typically converted into graphs through model-specific preprocessing. This couples simulator indexing, graph semantics, architecture, and action design, making representation choices difficult to reproduce or compare. We introduce *GraphIR*, a canonical intermediate representation that parses a Grid2Op state once, preserves equipment identity and terminal-level topology, and deterministically projects the state into task-specific graph views. GraphIR exposes representation as a controlled experimental variable while retaining traceability to simulator objects. Our evaluation isolates representation, system, and sampler effects through view ablations, flat and graph baselines, cross-environment validation, runtime measurements, and entity-level diagnostics.